End-To-End Self-tuning Self-supervised Time Series Anomaly Detection

arXiv:2404.02865v1 Announce Type: new
Abstract: Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A two-fold challenge for TSAD is a versatile and unsupervised model that can detect various different types of time series anomalies (spikes, discontinuities, trend shifts, etc.) without any labeled data. Modern neural networks have outstanding ability in modeling complex time series. Self-supervised models in particular tackle unsupervised TSAD by transforming the input via various augmentations to create …

abstract anomaly anomaly detection applications arxiv challenge cs.lg data detection environmental etc industry kpis monitoring patient sensors series time series trend type types unsupervised